def TrainUsingLR(xmls, annotatedxmlloc):
LR = LogisticRegressor()
trainer = Utils.Trainer.Trainer()
tableslist = list()
for xml in xmls:
tableslist.append(trainer.readAnnotatedxmlforTableDecomposition(annotatedxmlloc + xml + "_TD_ANNOTATED"))
LR.domaintrainforTableDecomposition(tableslist)
return LR
def TrainUsingLR(xmls, annotatedxmlloc):
LR = LogisticRegressor()
trainer = Utils.Trainer.Trainer()
tableslist = list()
for xml in xmls:
tableslist.append(
trainer.readAnnotatedxmlforTableDecomposition(annotatedxmlloc +
xml +
"_TD_ANNOTATED"))
LR.domaintrainforTableDecomposition(tableslist)
return LR
def TrainUsingLR(xmls, preprocessor, trainer, xmlloc, annotatedxmlloc):
LRImpl = LogisticRegressor()
annotatedxmllist = list()
for xmlname in xmls:
fontdict = preprocessor.getFontDictionary(ET.parse(xmlloc + xmlname + ".xml")) #list(pages), pages -> list(cols), col -> list(<Sparse/NonSparse, tag>)
annotatedxml = trainer.readAnnotatedXml(annotatedxmlloc + xmlname + "_annotated")
annotatedxmllist.append([annotatedxml, fontdict])
LRImpl.domaintrain(annotatedxmllist)
print LRImpl.trainedweights
f = open("TrainedWeightsLR", 'w')
for weight in LRImpl.trainedweights:
f.write(str(weight) + "\n")
f.close()
def TrainUsingLR(xmls, preprocessor, trainer, xmlloc, annotatedxmlloc):
LRImpl = LogisticRegressor()
annotatedxmllist = list()
for xmlname in xmls:
fontdict = preprocessor.getFontDictionary(
ET.parse(xmlloc + xmlname + ".xml")
) #list(pages), pages -> list(cols), col -> list(<Sparse/NonSparse, tag>)
annotatedxml = trainer.readAnnotatedXml(annotatedxmlloc + xmlname +
"_annotated")
annotatedxmllist.append([annotatedxml, fontdict])
LRImpl.domaintrain(annotatedxmllist)
print LRImpl.trainedweights
f = open("TrainedWeightsLR", 'w')
for weight in LRImpl.trainedweights:
f.write(str(weight) + "\n")
f.close()
def getModelwithTrainedWeights(isCRF=True):
trainedweights = list()
if (isCRF):
f = open("TrainedWeightsCRF", "r")
for weight in f:
trainedweights.append(float(weight))
f.close()
CRFImpl = CRF(trainedweights)
return CRFImpl
else:
f = open("TrainedWeightsLR", "r")
for weight in f:
trainedweights.append(float(weight))
f.close()
LR = LogisticRegressor(trainedweights)
return LR
